Training sets for supervised classification tasks are usually limited in scope and only contain
examples of a few classes. In practice, classes that were not seen in training are given …

We propose methods to improve the use of synthetic data for characterizing SAR image
exploitation in terms of various operating conditions (OCs). More specifically, we describe tools …

… Scherreik and Rigling [12] to include an expanded algorithm set with simulations across a
wider variety of conditions. Although Scherreik … and refer the reader to Scherreik [15] for more …

In a radar environment, cognitive receivers typically process raw IQ samples into sequences
of pulse descriptor words (PDWs). When there are multiple emitters present in the signal, …

Simulated pulse features (PFs) are commonly employed to develop signal exploitation
algorithms for classification, clustering, and other applications. Typically, an arbitrary constant …

Rotor-type small size Unmanned Aerial Vehicles (UAVs) for recreational purposes have
threatened people in public area by flying over them and crashing accidently without any safe …

Supervised multi-class target recognition algorithms label an input pattern according to the
most similar training class. Typically, the number of training classes is fixed and known a priori…

Semi-supervised learning approaches train on small sets of labeled data in addition to large
sets of unlabeled data. Self-training is a semi-supervised teacher-student approach that …

Automatic target recognition (ATR) performance models (PMs) help engineers and scientists
understand the effectiveness of their algorithms as a function of target, environment, and …

Clustering algorithms, such as Gaussian mixture models and K-means, often require the
number of clusters to be specified a priori. Bayesian nonparametric (BNP) methods avoid this …